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中文摘要：
对于粒子群优化算法易陷入局部最优的缺陷,反向学习策略对其的改进取得了较好的效果.然而,反向学习策略需要结合其它策略来提高算法后期的全局搜索能力,针对此缺陷,根据光的折射原理对反向学习策略的反向过程进行改进,提出反向学习的统一算法模型及基于折射原理反向学习模型的改进粒子群算法.实验与分析表明,与其它基于反向学习的粒子群算法相比,该模型更有效地改进了所提算法的全局搜索能力,提高了种群的多样性,从而提高了算法的收敛速度以及优化精度.
英文摘要：
One of shortcomings found in the particle swarm optimization algorithm is that it is easy to fall into local optimum,and the opposite learning strategy has a good effect on the improvement of this shortcoming. However,to improve the global search ability by using the opposite learning strategy it is necessary that in the late algorithm other strategies are combined to opposite learning strategy. To overcome this shortcoming,this paper improves the opposite process of the opposite learning strategy according to the refraction principle of light,and proposes the unified model of opposite-based learning（ UOBL） and the improved particle swarm optimization algorithm based on the opposite learning model of the principle of refraction（ refr PSO）. Experiment results and analysis showthat the model improves the global search ability of the refr PSO algorithm more effectively compared with other particle swarm algorithm based on opposite learning and the diversity of the population. Because of these improvements,the refr PSO enhances the convergence speed and the accuracy of optimization.
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An enhanced differential evolution algorithm for solving large scale optimisation problems on graphi
Parallel differential evolution with self-adapting control parameters and generalized opposition-bas
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